s = rpois(10000,lambda=250)
change = diff(s)/s[1:9999]
require(lattice)
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densityplot(change)
table( change > .23)
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22000 doctors A multiplication problem in converting patients per week to an annual basis. 708000 doctors Note 4 doctors per person. 600000 doctors and 4000 slots per medical school Using established rates of doctors per 1000 to assess “need.” Medical school estimate doesn’t take into account the working life of doctors. 200000 slots a year in medical school Note the big career drop out rate. *60000 slots per year 10% retirement rate.
AAMC document See page 11 for fall in number of physicians after Flexner report (175 to 125 per 100000). Figure 3 on page 12 for number of medical school graduates. Total physicians, see page 17: about 800000
Research article on sitting and mortality * Work through abstract * p. 1 Definition of activity metabolic equivalents * p. 1 Why “prospective” studies? How would you do an experiment about “television viewing”, “sitting in a car”, etc. * p. 2 Potential confounding variables: sex, age, education level, … * p. 2 “All … analysis were adjusted for sex, age, ….” * Table 1. Who sits a lot? People in poor health. People with little physical activity.
* Table 2. Note “dose/response”. Note consistency across categories. Oddball: 150-299 mn/wk of physical activity as a low Hazard ration for sitting > 11 hr per day. But look at the confidence interval. * Figure. Activity is good, sitting is bad, for both people in good health and people in bad health.
Table 5.1
Compare “never smoked” to “current smoker”. Rate ratio is 2.8 = 49.6/17.7. But the absolute difference in rate is 49.6-17.7 incidence rate per 100,000 person years.
Just like rate ratios but based on cumulative incidence. Example
Table 1.2
Another example: examining the affect of an intervention (Calling people to remind them of an immunization):
Table 5.2
showing affect of pressure bandages in patients undergoing coronary angiography.
A graphic on attributable risk: Use this to derive the attributable fraction as \(latex \frac{RR-1}{RR}\)
In the British Doctors Study: Table 5.4
Combine the rates of the different groups and compare to the background rate. Smoking and Tuberculosis in India. See the World Health Organization Exercise * WHO spreadsheet here
Why we do them? When the incidence/prevalence is very small, so a cohort is not efficient.
What’s an “odds”? From “Fugue for Tinhorns”I’m pickin’ Valentine, ’cause on the morning line
A guy has got him figured at five to nine
Example: Ovarian cancer and oral contraception use:
Example: Bicycle helmets and head injury
Why the Odds Ratio Approximates the Relative Risk (when the prevalence/incidence is very small): Box 5.5
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plotFun( log2((1 - ER*(1-OR))/(ER*(1-OR)*(1-ER)))~ OR&ER,
OR.lim=c(0.5,0.9), ER.lim=c(0.5, 0.9), main="log2(NNT)")